Faculty of Computing

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Author: search.filters.author.Kuruwitaarachchi, NSubject: search.filters.subject.Machine Learning

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    Real-time credit card fraud detection using machine learning
    (IEEE, 2019-01-10) Thennakoon, A; Bhagyani, C; Premadasa, S; Mihiranga, S; Kuruwitaarachchi, N
    Credit card fraud events take place frequently and then result in huge financial losses [1]. The number of online transactions has grown in large quantities and online credit card transactions holds a huge share of these transactions. Therefore, banks and financial institutions offer credit card fraud detection applications much value and demand. Fraudulent transactions can occur in various ways and can be put into different categories. This paper focuses on four main fraud occasions in real-world transactions. Each fraud is addressed using a series of machine learning models and the best method is selected via an evaluation. This evaluation provides a comprehensive guide to selecting an optimal algorithm with respect to the type of the frauds and we illustrate the evaluation with an appropriate performance measure. Another major key area that we address in our project is real-time credit card fraud detection. For this, we take the use of predictive analytics done by the implemented machine learning models and an API module to decide if a particular transaction is genuine or fraudulent. We also assess a novel strategy that effectively addresses the skewed distribution of data. The data used in our experiments come from a financial institution according to a confidential disclosure agreement.
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    Predicting Diabetes Mellitus Using Machine Learning and Optical Character Recognition
    (IEEE, 2021-04-02) Silva, W. A. J. R; Shirantha, H. M. K; Balalla, L. J. M. V. N; Ranasinghe, R. A. D. V. K; Kuruwitaarachchi, N; Kasthurirathna, D
    Diabetes Mellitus is recognized as a chronic metabolic disease that is characterized by hyperglycemia. As stated by the International Diabetes Federation, the statistics reveal that the incidence of diabetes among adults in Sri Lanka is 8.5%. In hindsight, this indicates that an average of one in every twelve adults in Sri Lanka is at risk of being diagnosed with the disease. However, presently, due to the lack of knowledge or mediums concerning the disease and its symptoms, diabetes often goes undetected which has resulted in 1/3 rd of the constituent population being unaware that they possess the disease. The proposed system aims to implement an application to read and analyze medical reports which will generate data that predicts the probabilities of the contraction and onset of diabetes, with insurance of maximum system efficiency and data credibility. Machine learning classification algorithms and optimization techniques have been used to predict diabetes status with maximum accuracy. To extract data from medical reports Optical Character Recognition, Image Processing, and Natural Language Processing have been used